SIDE was first introduced by Saxena et al. [1]} and it gained momentum from the work by Eigen et al. [2]}, [3]}. Since then, the number of related studies has grown rapidly [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}. At first, the proposed SIDE methods improved the accuracy by employing large architectures [4]}, [10]} and more complex encoding-decoding schemes [11]}. Then, they started to diverge into using semantic labels [9]}, exploiting the relationship between depth and surface normal [6]}, reformulating as a classification problem [5]} or mixing both [7]}. Other studies suggested to estimate relative depth [8]} or to learn calibration patterns to improve the generalization ability. Recent SIDE approaches exploit monocular priors such as occlusion [13]}, and planar structures either explicitly [14]}, [15]}, [29]} or implicitly [17]}. Despite these efforts, SIDE still generalizes quite poorly to unseen data. In this work, we leverage SIDE's ability to produce dense depth estimations and inject it with a small set of depth measurements to boost the accuracy while further shrinking the network size.
